1. Field of the Invention
The present invention relates generally to noninvasive blood and tissue analyte determination. More particularly, the invention relates to methods and apparatus for detecting conditions leading to erroneous noninvasive tissue analyte measurements.
2. Description of Related Art
Previously we reported an invention for measuring glucose noninvasively through an intelligent measurement system (IMS) in S. Malin, T. Ruchti, An intelligent system for noninvasive blood analyte prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001). The IMS involved the classification of patients into a multiplicity of xe2x80x9cbinsxe2x80x9d or xe2x80x9cclassesxe2x80x9d and the application of a suitable calibration model. A key element of the IMS is a Performance Monitor capable of detecting poor instrument performance, patient sampling errors, and other anomalies leading to an invalid or degraded glucose measurement. Here we describe a novel method for detecting and mitigating the wide range of potential errors associated with the in vivo application of an instrument for the noninvasive measurement of glucose.
The error detection system (EDS) operates on a near infrared measurement of in vivo skin tissue. The architecture employs a pattern classification engine and hierarchy of levels to analyze, detect, and diagnose instrument, interface, and sample errors manifested in the near infrared measurement. A priori information about the sources of errors is used to establish preset limits and categories of errors. Application of the system results in improved noninvasive glucose measurement accuracy through the rejection of invalid and poor samples.
Noninvasive Measurement of Glucose
Diabetes is a leading cause of death and disability worldwide and afflicts an estimated 16 million Americans. Complications of diabetes include heart and kidney disease, blindness, nerve damage, and high blood pressure with the estimated total cost to United States economy alone exceeding $90 billion per year [Diabetes Statistics, Publication No. 98-3926, National Institutes of Health, Bethesda Md. (November 1997)]. Long-term clinical studies show that the onset of complications can be significantly reduced through proper control of blood glucose levels [The Diabetes Control and Complications Trial Research Group, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Eng J of Med, 329:977-86 (1993)]. A vital element of diabetes management is the self-monitoring of blood glucose levels by diabetics in the home environment. A significant disadvantage of current monitoring techniques is that they discourage regular use due to the inconvenient and painful nature of drawing blood through the skin prior to analysis. Therefore, new methods for self-monitoring of blood glucose levels are required to improve the prospects for more rigorous control of blood glucose in diabetic patients.
Numerous approaches have been explored for measuring glucose levels in vivo, ranging from invasive methods such as micro dialysis to noninvasive technologies that rely on spectroscopy. Each method has associated advantages and disadvantages, but only a few have received approval from certifying agencies. To date, no noninvasive techniques for the self-monitoring of blood glucose have been certified.
One method, near-infrared spectroscopy involves the illumination of a spot on the body with near-infrared electromagnetic radiation (light in the wavelength range 700-0.2500 nm). The light is partially absorbed and scattered, according to its interaction with the constituents of the tissue prior to being reflected back to a detector. The detected light contains quantitative information that is based on the known interaction of the incident light with components of the body tissue including water, fats, protein, and glucose.
Previously reported methods for the noninvasive measurement of glucose through near-infrared spectroscopy rely on the detection of the magnitude of light attenuation caused by the absorption signature of blood glucose as represented in the targeted tissue volume. The tissue volume is the portion of irradiated tissue from which light is reflected or transmitted to the spectrometer detection system. The signal due to the absorption of glucose is extracted from the spectral measurement through various methods of signal processing and one or more mathematical models. The models are developed through the process of calibration on the basis of an exemplary set of spectral measurements and associated reference blood glucose values (the calibration set) based on an analysis of capillary (fingertip), or venous blood.
Near-infrared spectroscopy has been demonstrated in specific studies to represent a feasible and promising approach to the noninvasive prediction of blood glucose levels. M. Robinson, R. Eaton, D. Haaland, G. Keep, E. Thomas, B. Stalled, P. Robinson, Noninvasive glucose monitoring in diabetic patients: A preliminary evaluation, Clin Chem, 38:1618-22 (1992) reports three different instrument configurations for measuring diffuse transmittance through the finger in the 600-1300 nm range. Meal tolerance tests were used to perturb the glucose levels of three subjects and calibration models were constructed specific to each subject on single days and tested through cross-validation. Absolute average prediction errors ranged from 19.8 to 37.8 mg/dL. H. Heise, R. Marbach, T. Koschinsky, F. Gries, Noninvasive blood glucose sensors based on near-infrared spectroscopy, Artif Org, 18:439-47 (1994); H. Heise, R. Marbach, Effect of data pretreatment on the noninvasive blood glucose measurement by diffuse reflectance near-IR spectroscopy, SPIE Proc, 2089:114-5 (1994); R. Marbach, T. Koschinsky, F. Gries, H. Heise, Noninvasive glucose assay by near-infrared diffuse reflectance spectroscopy of the human inner lip, Appl Spectrosc, 47:875-81 (1993) and R. Marbach, H. Heise, Optical diffuse reflectance accessory for measurements of skin tissue by near-infrared spectroscopy, Applied Optics 34(4):610-21 (1995) present results through a diffuse reflectance measurement of the oral mucosa in the 1111-1835 nm range with an optimized diffuse reflectance accessory. In vivo experiments were conducted on single diabetics using glucose tolerance tests and on a population of 133 different subjects. The best standard error of prediction reported was 43 mg/dL and was obtained from a two-day single person oral glucose tolerance test that was evaluated through cross-validation.
K. Jagemann, C. Fischbacker, K. Danzer, U. Muller, B. Mertes, Application of near-infrared spectroscopy for noninvasive determination of blood/tissue glucose using neural network, Z Phys Chem, 191 S: 179-190 (1995); C. Fischbacker, K. Jagemann, K. Danzer, U. Muller, L. Papenkrodt, J. Schuler, Enhancing calibration models for noninvasive near-infrared spectroscopic blood glucose determinations, Fresenius J Anal Chem 359:78-82 (1997); K. Danzer, C. Fischbacker, K. Jagemann, K. Reichelt, Near-infrared diffuse reflection spectroscopy for noninvasive blood-glucose monitoring, LEOS Newsletter 12(2):9-11 (1998); and U. Muller, B. Mertes, C. Fischbacker, K. Jagemann, K. Danzer, Noninvasive blood glucose monitoring by means of new infrared spectroscopic methods for improving the reliability of the calibration models, Int J Artif Organs, 20:285-290 (1997) recorded spectra in diffuse reflectance over the 800-1350 nm range on the middle finger of the right hand with a fiber-optic probe. Each experiment involved a diabetic subject and was conducted over a single day with perturbation of blood glucose levels through carbohydrate loading. Results, using both partial least squares regression and radial basis function neural networks were evaluated on single subjects over single days through cross-validation. Danzer, et al., supra, report an average root mean square prediction error of 36 mg/dL through cross-validation over 31 glucose profiles.
J. Burmeister, M. Arnold, G. Small, Human noninvasive measurement of glucose using near infrared spectroscopy [abstract], Pittcon, New Orleans La. (1998) collected absorbance spectra through a transmission measurement of the tongue in the 1429-2000 nm range. A study of five diabetic subjects was conducted over a 39-day period with five samples taken per day. Every fifth sample was used for an independent test set and the standard error of prediction for all subjects was greater than 54 mg/dl.
In T. Blank, T. Ruchti, S. Malin, S. Monfre, The use of near-infrared diffuse reflectance for the noninvasive prediction of blood glucose, IEEE Lasers and Electro-Optics Society Newsletter, 13:5 (October 1999), the reported studies demonstrate noninvasive measurement of blood glucose during modified oral glucose tolerance tests over a short time period. The calibration was customized for the individual and tested over a relatively short time period.
In all of these studies, limitations were cited that would affect the acceptance of such a method as a commercial product. These limitations included sensitivity, sampling problems, time lag, calibration bias, long-term reproducibility, and instrument noise. Fundamentally, however, accurate noninvasive estimation of blood glucose is presently limited by the available near-infrared technology, the trace concentration of glucose relative to other constituents and the dynamic nature of the skin and living tissue of the patient (for example, see O. Khalil, Spectroscopic and clinical aspects of noninvasive glucose measurements, Clin Chem, 45:165-77 (1999)). As reported by S. Malin, T. Ruchti, An Intelligent System for Noninvasive Blood Analyte Prediction, U.S. Pat. No. 6,280,381 (Aug. 28, 2001), the entirety of which is hereby incorporated by reference, chemical, structural, and physiological variations occur that produce dramatic and nonlinear changes in the optical properties of the tissue sample [see R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, 7:1, pp.13-19 (1981), W. Cheong, S. Prahl, A. Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, 26:12, pp.2166-2185, (December 1990), D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue using time-resolved spectrophotometry: the impact of statically and dynamically variable optical path lengths, SPIE, 1888, pp.10-21 (1993), J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, 40, pp.1123-1140 (December 1984), S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, 1:4, pp.418-424 (October 1996), A. Profio, Light transport in tissue, Applied Optics, 28:12), pp. 2216-2222, (June 1989), M. Van Gemert, S. Jacques, H. Sterenborg, W. Star, Skin optics, IEEE Transactions on Biomedical Engineering, 36:12, pp.1146-1154 (December 1989), and B. Wilson, S. Jacques, Optical reflectance and transmittance of tissues: principles and applications, IEEE Journal of Quantum Electronics, 26:12, pp. 2186-2199].
The measurement is further complicated by the heterogeneity of the sample, the multi-layered structure of the skin, and the rapid variation related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, temperature fluctuations, and blood analyte levels. This can be further considered through a discussion of the various properties of the skin, sample error, and instrumentation related issue.
Skin Structure
The structure and composition of skin varies widely among individuals as well as between different sites and over time on the same individual. Skin consists of a superficial layer known as the stratum corneum, a stratified cellular epidermis, and an underlying dermis of connective tissue. Below the dermis is the subcutaneous fatty layer or adipose tissue. The epidermis, with a thickness of 10-150 xcexcm, together with the stratum corneum provides a barrier to infection and loss of moisture, while the dermis is the thick inner layer that provides mechanical strength and elasticity [F. Ebling, The Normal Skin, Textbook of Dermatology, 2nd ed.; A. Rook; D. Wilkinson, F. Ebling, Eds.; Blackwell Scientific, Oxford, pp 4-24 (1972)]. In humans, the thickness of the dermis ranges from 0.5 mm over the eyelid to 4 mm on the back and averages approximately 1.2 mm over most of the body [S. Wilson, V. Spence, Phys. Med. Biol., 33:894-897 (1988)].
In the dermis, water accounts for approximately 70% percent of the volume. The next most abundant constituent is collagen, a fibrous protein comprising 70-75% of the dry weight of the dermis. Elastin fibers, also a protein, are plentiful though they constitute only a small proportion of the bulk. In addition, the dermis contains a wide variety of structures (e.g., sweat glands, hair follicles and blood vessels) and other cellular constituents [see F. Ebling, supra]. Conversely, the subcutaneous layer (adipose tissue) is by volume approximately 10% water and consists primarily of cells rich in triglycerides (fat). The concentration of glucose varies in each layer according to the water content, the relative sizes of the fluid compartments, the distribution of capillaries and the perfusion of blood. Due to the high concentration of fat, the average concentration of glucose in subcutaneous tissue is significantly lower than that of the dermis.
Scattering Properties of Skin
When a beam of light beam is directed onto the skin surface, a part of it is reflected while the remaining part penetrates the skin. The proportion of reflected light energy is strongly dependent on the angle of incidence. At nearly perpendicular incidence, about 4% of the incident beam is reflected due to the change in refractive index between air (xcex7D=1.0) and dry stratum corneum (xcex7D=1.55). For normally incident radiation, this xe2x80x9cspecular reflectancexe2x80x9d component may be as high as 7%, because the very rigid and irregular surface of the stratum corneum produces off-normal angles of incidence. Regardless of skin color, specular reflectance of a nearly perpendicular beam from normal skin is always between 4-7% over the entire spectrum from 250-3000 nm. See R. Scheuplein, J. Soc. Cosmet. Chem., v.15, pp. 111-122 (1964). Only the air-stratum corneum border gives rise to a regular reflection. Results from a previous study indicate that the indices of refraction of most soft tissue (skin, liver, heart, etc) lie within the 1.38-1.41 range with the exception of adipose tissue, which has a refractive index of approximately 1.46. See J. Parrish, R. Anderson, F. Urbach, D. Pitts, UV-A: Biologic Effects of Ultraviolet Radiation with Emphasis on Human Responses to Longwave Ultraviolet, New York, Plenum Press (1978). Therefore, these differences in refractive index between the different layers of the skin are too small to give a noticeable reflection. See Ebling, supra. The differences are expected to be even more insignificant when the stratum corneum is hydrated, owing to refractive index matching.
The 93-96% of the incident beam that enters the skin is attenuated due to absorption or scattering within any of the layers of the skin. These two processes taken together essentially determine the penetration of light into skin, as well as remittance of scattered light from the skin. Diffuse reflectance or remittance is defined as that fraction of incident optical radiation that is returned from a turbid sample. Absorption by the various skin constituents mentioned above account for the spectral extinction of the beam within each layer. Scattering is the only process by which the beam may be returned to contribute to the diffuse reflectance of the skin. Scattering results from differences in a medium""s refractive index, corresponding to differences in the physical characteristics of the particles that make up the medium. The spatial distribution and intensity of scattered light depends upon the size and shape of the particles relative to the wavelength and upon the difference in refractive index between the medium and the constituent particles.
The scattering coefficient of biological tissue depends on many uncontrollable factors, which include the concentration of interstitial water, the density of structural fibers, and the shapes and sizes of cellular structures. Scattering by collagen fibers is of major importance in determining the penetration of optical radiation within the dermis. See F. Bolin, L. Preuss, R. Taylor, R. Ference, Appl. Opt, v. 28, pp. 2297-2303 (1989). The greater the diffusing power of a medium, the greater will be the absorption due to multiple internal reflections. Therefore, reflectance values measured on different sites on the same person, or from the same site on different people, can differ substantially even when the target absorber is present in the same concentration. These differences can be attributed to gender, age, genetics, disease, and exogenous factors due to lifestyle differences. For example, it is known that skin thickness in humans is greater in males than females, whereas the subcutaneous fat thickness is greater in females. The same group reports that collagen density, the packing of fibrils in the dermis, is higher in the forearms of males than females. See S Schuster, M. Black, E. McVitie, Br. J. Dermatol, v.93, pp.639-643, (1975).
Dynamic Properties of the Skin
While knowledge of and utilization of the optical properties of the skin, high instrument sensitivity and compensation for inherent nonlinearities are all vital for the application of near-infrared spectroscopy to noninvasive blood analyte measurement, an understanding of biological and chemical mechanisms that lead to time dependent changes in the optical properties of skin tissue is equally important and, yet, largely ignored. At a given measurement site, skin tissue is often assumed to be static except for changes in the target analyte and other absorbing species. However, variations in the physiological state of tissue profoundly affect the optical properties of tissue layers and compartments over a relatively short period of time.
Such variations are often dominated by fluid compartment equalization through water shifts and are related to hydration levels and changes in blood analyte levels.
Total body water accounts for over 60% of the weight of the average person and is distributed between two major compartments: the extracellular fluid (one-third of total body water) and the intracellular fluid (two-thirds of total body water) [see A. Guyton, J. Hall, Textbook of Medical of Physiology, 9th ed., Philadelphia, W. B. Saunders Company (1996)]. The extracellular fluid in turn is divided into the interstitial fluid (extravascular) and the blood plasma (intravascular). Water permeable lipid membranes separate the compartments and water is transferred rapidly between them through the process of diffusion, in order to equalize the concentrations of water and other analytes across the membrane. The net water flux from one compartment to another constitutes the process of osmosis and the amount of pressure required to prevent osmosis is termed the osmotic pressure. Under static physiological conditions the fluid compartments are at equilibrium. However, during a net fluid gain or loss as a result of water intake or loss, all compartments gain or lose water proportionally and maintain a constant relative volume.
A mechanism for distributing substances contained in blood serum that are needed by the tissues, such as water and glucose, is through the process of diffusion. The invention recognizes that Fick""s law of diffusion drives the short-term intra-/extra vascular fluid compartment balance. The movement of water and other analytes from intravascular to extravascular compartments occurs rapidly as tremendous numbers of molecules of water and other constituents, in constant thermal motion, diffuse back and forth through the capillary wall. On average, the rate at which water molecules diffuse through the capillary membrane is about eighty times greater than the rate at which the plasma itself flows linearly along the capillary. In the Fick""s Law expression, the actual diffusion flux, IOA, is proportional to the concentration gradient, dc/dx between the two compartments and the diffusivity of the molecule, DA according to the equation                               I          OA                =                  -                                                    D                A                            ⁡                              (                                                      ⅆ                    c                                                        ⅆ                    x                                                  )                                      .                                              (        1        )            
Short-term increases (or decreases) in blood glucose concentrations lead to an increase (or decrease) in blood osmolality (number of molecules per unit mass of water). Fluid is rapidly re-distributed accordingly and results in a change in the water concentration of each body compartment. For example, the osmotic effect of hyperglycemia is a movement of extravascular water to the intravascular space. Conversely, a decrease in blood glucose concentration leads to a movement of water to extravascular space from the intravascular compartment.
Because the cell membrane is relatively impermeable to most solutes but highly permeable to water, whenever there is a higher concentration of a solute on one side of the cell membrane, water diffuses across the membrane toward the region of higher solute concentration. Large osmotic pressures can develop across the cell membrane with relatively small changes in the concentration of solutes in the extracellular fluid. As a result, relatively small changes in concentration of impermeable solutes in the extracellular fluid, such as glucose, can cause tremendous changes in cell volume.
Sampling Error
Noninvasive measurement of tissue properties and analytes, such as blood glucose concentration, may employ NIR spectroscopic methods. S. Malin, T. Ruchti, U.S. Pat. No. 6,280,381, supra, describes a system for noninvasively measuring blood glucose concentrations in vivo, using NIR spectral analysis. Such NIR spectroscopy-based methods utilize calibrations that are developed using repeated in vivo optical samples of the same tissue volume. These successive measurements must yield a substantially repeatable spectrum in order to produce a usable calibration. As herein described, the heterogeneous and dynamic nature of living human skin leads to sampling uncertainty in the in vivo measurement. As previously described, sampling differences can arise due to variable chemical composition and light scattering properties in tissue. As an example: because glucose is not uniformly distributed in tissue, a variation in the volume of tissue sampled is likely to lead to a variation in the strength of the glucose signal, even though glucose concentration in the tissue or blood remains constant. Variation in the repeated placement of the optical probe used for sampling at the measuring surface site can lead to sampling in errors in two separate ways: first, variations in location of the probe can cause a different tissue volume to be sampled, and second, varying the amount of pressure applied by the probe on the tissue can alter the optical scattering by the tissue, thereby changing the sampled tissue volume. A change in optical sampling may lead to a variation in the spectral signal for a target analyte even though the concentration of the analyte in the blood or tissue remains unchanged. Furthermore, air gaps between the surface of the optical probe and the surface of the tissue being sampled give rise to variable surface reflection. Variable surface reflection leads to a variable light launch into the tissue that in turn gives rise to an increase in the nonlinear nature of the spectral measurements. Certainly, a variable nonlinear measurement would be very difficult to calibrate.
Commercialization of Near-infrared Instrumentation
The noninvasive measurement of trace analytes, such as glucose, through near-infrared technology, requires a stable spectroscopic measurement system with a high signal-to-noise ratio (greater than 20,000-to-1 measured as the dynamic range divided by the RMS noise). Interference related to instrument malfunction or environmental influences on the instrumentation cause uncertainty in the affected measurements. Therefore, the development of a robust apparatus with the necessary performance characteristics is exceedingly challenging. In addition, a device that is suitable for use in clinics and the diverse environments experienced by the consumer must be durable, stable and robust despite environmental fluctuations and user misuse.
The Problem
The noninvasive measurement of glucose is challenging due to the complex structure of the sample, the presence of interfering analytes, the dynamic nature of tissue, the small signal related to glucose, physiological conditions interfering with the measurement and instrumental effects leading to uncertainty. Any one of the aforementioned effects has the potential under a variety of conditions of influencing the spectroscopic measurement and introducing error into the noninvasive determination of glucose. Various means for compensating for such variation have been reported. For example, T. Blank, G. Acosta, M. Mattu, M. Makarewicz, S. Monfre, A. Lorenz, T. Ruchti, Optical sampling interface system for in vivo measurement of tissue, U.S. patent application Ser. No. 10/170,921 (Jun. 12, 2002), describe a system for reducing variation related to surface effects, improving the uniformity of surface hydration and providing a precise sampling measurement. However, even with the development and use of compensations for reducing both the probability and magnitude of the effects related to measurement error, each source of measurement interference can cause an erroneous measurement under extreme or exaggerated circumstances. Furthermore, the potential for the occurrence of erroneous measurements has a profound and detrimental affect on the safety and efficacy of the medical application of noninvasive systems for measurement of glucose and without remediation severely limits the potential usage of this technology in therapeutic and diagnostic applications. Therefore, the presence of conditions leading to erroneous measurements must be detected through a comprehensive system that identifies conditions likely to lead to an erroneous measurement.
In view of the problems left unsolved by the prior art, there exists a need for a method and apparatus to detect conditions leading to erroneous noninvasive glucose measurements. Furthermore, it would be a significant advancement to provide a comprehensive error detection system capable of diagnosing the source and nature of the error.
The error detection system (EDS) operates on a near infrared measurement of in vivo skin tissue. The architecture employs a pattern classification engine and hierarchy of levels to analyze, detect and diagnose instrument, interface and sample errors manifested in the near-infrared measurement. A priori information about the sources of errors is used to establish preset limits and categories of errors. Application of the system results in improved prediction accuracy through the rejection of invalid and poor samples.
The invention involves a noninvasive near-infrared glucose meter, an error detection system (EDS), a system for diagnosing and mitigating errors and a reporting method (FIGS. 1-3). The meter is a near-infrared spectrometer that makes a near-infrared based measurement of the patient""s skin tissue. The error detection system, shown in FIG. 6, performs a series of tests based upon an ordered hierarchy to determine the suitability of the near-infrared measurement for blood glucose measurement. The final component of the system evaluates the error condition, diagnoses the specific mode of failure (if necessary) and reports the actions to be taken (FIG. 8).
The system of the invention is organized into a hierarchy of levels that receive and inherit information from lower levels (FIG. 2). The lowest level is applied to the near-infrared measurements (or spectra) of all patients (i.e., it is not customized to the patient) on the basis of rudimentary specifications required for acceptable noninvasive blood glucose measurement. The mid-level utilizes patient history information to perform a more detailed examination of the data for errors and the upper-level uses the calibration database to assess the acceptability of the data for glucose measurement.
Errors generated by each level are inherited by succeeding (higher) levels for error diagnosis until a critical error is encountered (one that prohibits further analysis). The generated error leads to various actions for mitigating the problem from simple sample rejections to patient re-calibration. As illustrated in FIG. 2, the level of the error dictates the highest level of prescribed action.
The system is composed of a multiplicity of elements, each involved in an aspect of the system of error detection. Each sub-component involves a test to determine whether or not an error condition has occurred on the basis of a calculated variable and a statistically based or empirically determined range of acceptability. A distinguishing quality of the sub-components is that they were derived from the a priori knowledge of the optical properties of tissue, the basis of spectroscopic measurement and calibration, tissue physiology, the effect of structural deformity of the tissue sample, and the necessary requirements for the non-invasive measurement of glucose. Therefore, the majority of the error conditions also provide useful information for diagnosing the source of the error. The composite of the sub-component outcomes serves as an input for a knowledge base system used to diagnose the specific source of error. Finally, with the source of error diagnosed, a database is used to provide corrective instructions.
In addition, each sub-component and each level of the error detection system is capable of operating independently of the other levels and sub-components to the benefit of a noninvasive glucose measurement system (see FIG. 3). For example, in FIG. 6, the low-level subsystem is useful apart from the more sophisticated levels in detecting instrumental malfunctions and gross sampling errors and, as shown in FIG. 3C, can be applied in a manner that is independent of the other levels. Similarly, the mid and high-levels can be used apart from the other levels. Alternately, each individual element of FIG. 8 can be used as a method for error detection apart from the other elements. Therefore, the invention alternately provides a system, sub-systems (levels) and individual processes for detecting errors during non-invasive glucose measurement.